Studies show 40% of the women leave STEM just within 5 years of starting. So even today where we are statistically to have over 50% women representation in undergraduate STEM degree programs we still have only 28 % women in actual jobs. So we are getting more girls in a bucket that is leaking. So keeping women in STEM is more important than getting more girls in STEM.
In both secondary and high school, students choose to study engineering depending on how capable they see themselves. This fact is more recognizable in girls, who tend to underestimate themselves. Hence, girls do not normally think about not choosing engineering due to their difficulty but because they think they are not skilled enough to do it. Educational institutions—with undoubtedly many well-intentioned educators—are themselves complicit in reinforcing the hurdles. In order to dismantle these barriers, we likely need educators at all levels of education to examine their own biases and stereotypes
Hence our team came up with Project ** Sista: sisters for each other!**
The problem it solves:
It aims to solve the problem ** Why do so many women leave their careers in STEM? ** followed by fixing the issues such as:
- Problems of female education
- Lack of strong female role models
- Lack of workspace relationship
- Lack of mentorship and motivation
- Imposter Syndrome
What it does
Sista is a web application with an integrated Machine Learning model where young girls can get exposure to various tech communities based on their interests. They will be mentored along with other fellow peers with a group of mentors cum their female role models. This would not only instill in them a love of technology at a very young age but also would eradicate problems like lack of motivation, guidance, and imposter syndrome.
How we built it
Figma: After brainstorming many ideas, we moved on to wireframing in Figma, starting with lo-fidelity and working together to create clickable interactions on the high fidelity prototype.
Machine Learning: implemented K-Means Clustering Algorithm using python libraries: Pandas, Matplotlib, Numpy.
Front-end development using React.js
Material-UI and CSS: we worked on the UI/UX, layout, CSS, and design.
Challenges we ran into
- We faced difficulty in implementing the Kmean algorithm.
- Debugging issue and dynamic routing in React.js.
- Firebase authentication
Accomplishments that we're proud of
✅We are proud that we were able to address such an important issue and find a practical and inclusive solution to it.
✅Our teamwork and cooperative workflow helped us to build the project in its entirety.
✅We are proud to have completed the whole UI design, develop and publish a fully functional website and developed an ML model.
What we learned
- The very first thing we all learned was teamwork. We thought about the various problems around us and came up with a solution that we could build in the given time.
- The second thing we could implement Machine Learning Algorithm to our project. We learned better tactics of collaboration and brainstorming. We also learned a lot of facts about mental health while researching, that we were unaware of. We learned about the problems people with mental illnesses face and what solutions they have available. ## What's next for Sista We want to add more functionality and flexibility to our web application. Integrating Machine learning model to our application with a dataset of people with common interest. We would like to approach women tech leaders, volunteers who are passionate about women in STEM, and organizations for collaboration so that they can support and mentor women through our web application. We plan to add a chat server API.